Can recurrent neural network enhanced EEGNet improve the accuracy of ERP classification task? An exploration and a discussion

Zhao, Haifeng; Yang, Yikai; Karlsson, Petra; McEwan, Alistair

doi:10.1007/s12553-020-00458-x

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Other types of ERPs, such as motor imagery, also induce recognizable waveforms. Prior deep-learning-based methods have deployed various classifiers to distinguish between different ERP types (Lawhern et al, 2018 ; Santamaŕıa-Vázquez et al, 2019 , 2020 ; Wen et al, 2019 ; Zhao et al, 2020 ; Zang et al, 2021 ). Oscillatory BCIs can detect the signal power of EEG frequency bands.…”

Section: Related Workmentioning

confidence: 99%

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction

Liu

Höllerer

Sra

2022

Front. Comput. Neurosci.

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Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task.

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Section: Related Workmentioning

confidence: 99%

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction

Liu

Höllerer

Sra

2022

Front. Comput. Neurosci.

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“…The artificial neural network has been extensively applied in medical diagnosis due to its outstanding large-scale data processing, high computation speed and strong fault tolerance. Many researchers have published literature on neural networks involving Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and others to diagnose ASD [12][13][14][15][16][17][18][19][20] .…”

Section: Models Based On Neural Workmentioning

confidence: 99%

Comparison of different classification methods for autism spectrum diagnosis

Liu

Wang²,

Tian³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Studies have found autism spectrum disorder is a diffuse developmental disease of the central nervous system. The majority of autism cases result from a combination of genetic predisposition and environmental factors that influence early brain development, despite a few being caused by genes alone. Traditional diagnosis of autism spectrum disorder is usually through interviews and questionnaires, which takes plenty of time and might be misdiagnosed. The primary purpose of this study is to compare different classification methods for distinguishing autism spectrum disorder from typical development by machine learning and deep learning in recent years. The experiments are conducted to discuss their strengths and weaknesses, which, in turn, results are presented for further research.

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“…Technological advances have led to significant evolution in user–computer interfaces. Hence, there are new opportunities to facilitate and simplify computer access, including the practical use of these interfaces in vast applications [ 4 , 5 ] and a broad spectrum of user communities. Designing models that can predict brain activity is an extensive research field, where one crucial aspect is feature selection, which is used to find the patterns that describe data.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Architecture Reduction for fMRI Data

Alvarez-Gonzalez

Méndez-Vázquez

2022

Brain Sciences

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In recent years, deep learning models have demonstrated an inherently better ability to tackle non-linear classification tasks, due to advances in deep learning architectures. However, much remains to be achieved, especially in designing deep convolutional neural network (CNN) configurations. The number of hyper-parameters that need to be optimized to achieve accuracy in classification problems increases with every layer used, and the selection of kernels in each CNN layer has an impact on the overall CNN performance in the training stage, as well as in the classification process. When a popular classifier fails to perform acceptably in practical applications, it may be due to deficiencies in the algorithm and data processing. Thus, understanding the feature extraction process provides insights to help optimize pre-trained architectures, better generalize the models, and obtain the context of each layer’s features. In this work, we aim to improve feature extraction through the use of a texture amortization map (TAM). An algorithm was developed to obtain characteristics from the filters amortizing the filter’s effect depending on the texture of the neighboring pixels. From the initial algorithm, a novel geometric classification score (GCS) was developed, in order to obtain a measure that indicates the effect of one class on another in a classification problem, in terms of the complexity of the learnability in every layer of the deep learning architecture. For this, we assume that all the data transformations in the inner layers still belong to a Euclidean space. In this scenario, we can evaluate which layers provide the best transformations in a CNN, allowing us to reduce the weights of the deep learning architecture using the geometric hypothesis.

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Can recurrent neural network enhanced EEGNet improve the accuracy of ERP classification task? An exploration and a discussion

Cited by 10 publications

References 35 publications

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction

Comparison of different classification methods for autism spectrum diagnosis

Deep Learning Architecture Reduction for fMRI Data

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